It is often desirable, or even necessary, to migrate data from one data storage system to another. For instance, a company may need to migrate their data from an older, out of date, storage system to a newer, more modern and secure, data system. In another scenario, a parent company may acquire another company, in which case the data that is stored on the storage servers of the company that has been bought out need to be migrated to the parent company's data storage system. And in another scenario, patient data may be migrated from the data storage systems from many different hospitals to a central data storage system for consolidating the patient data.
In any scenario, data migration can be a major project as it may require the transfer of large amounts of data from one data storage system to another data storage system. Migrating large amounts of data may also require a significant investment of human resource talent to plan and oversee the data migration project. However, investing a significant portion of a company's human resource talent into a large data migration project has many pitfalls.
For one, the significant drain of human resource talent may be overly expensive for the company to absorb. In addition, exposing the data migration project to so many human workers leaves the data migration project vulnerable to human errors.
Therefore, there is a need to introduce a more efficient data migration process that is able to utilize computing resources to take on a greater role in planning, validating and executing a data migration project.